DWH automation is wonderful, but which tool? Using 13 typical data constellations, a fictitious data model is presented that can be used to evaluate the implementation performance of an automation tool. In 4 follow-up presentations, one implementation each will be presented based on the data.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic

Extended Abstract:
The automation of data warehouse development, especially in connection with Data Vault, drastically increases the speed of development in BI. Before using this technology, the question of the right tool for DWH automation arises:

• Does the tool fulfil all the necessary functionalities?
• What are the necessary functionalities in the first place?
• What suits the existing team and the company technology or culture?

Based on several tool evaluations and with a lot of know-how about typical Data Vault problems, the German Data Vault User Group (DDVUG) has created a fictitious company: Willibald - seeds and plants.
A data model with 13 typical problems is available for this purpose. Based on these typical problems, the implementations within the DWH automation tools can be evaluated.
In this presentation, these challenges are described and a catalogue of criteria for selection and evaluation is presented. In four further presentations, users/manufacturers will show how the challenge was implemented, giving an impression of how to use the tool and an insight into general functions.
This is an ideal start to your own tool selection as well as an ideal opportunity to get a tool overview or to update your own tool overview.

With the data of the fictitious company Willibald and its 13 challenges, a DWH is created with datavaultbuilder. An ideal opportunity to see and evaluate the tool in realistic use. The data of the company Willibald has been explained by DDVUG in session 1 previous lecture.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic

Extended Abstract:
The fictitious company Willibald has asked Datavault Builder to perform a POC for their new DWH. For this purpose, it has defined 13 typical challenges to be solved. In this talk we will present how the task was solved in Datavault Builder. For this, we agilely create a business model, which is automatically converted into working code. We load and analyse data, present the created documentation and data lineage and give an outlook on how the solution can be rolled out and operated.
About Datavault Builder
Datavault Builder is the award-winning business-driven data warehouse automation solution that allows you to trust your data and trust your decisions.
It is an efficient visual data integration solution replacing up to nine different software tools. Datavault Builder leverages standardization and cooperation between business and IT people as the key to success.
Increase productivity and benefit from the fastest possible time-to-insights without compromising on full auditability.

With the data of the fictitious company Willibald and its 13 challenges, a DWH is created with the open source software dbt. An ideal opportunity to see and evaluate the tool in realistic use. The data of the company Willibald will be explained by DDVUG in a previous lecture.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic

Extended Abstract:
For the fictitious company Willibald, a complete data model with data is available that was created by DDVUG and provided with 13 typical challenges. In this presentation, a possible solution using dbt, datavault4dbt and our own extensions will be shown and combined into a complete data warehouse. This will give you an overall impression and the possibility to get your own comprehensive impression of the tool. 
 Dbt (data build tool) is a development framework that enables teams to build, test and deploy analytics solutions faster. It can be used to create a unified development environment for all data engineering and data analyst teams. Scalefree's open-source Datavault4dbt package extends dbt with dbt macros to build a Data Vault 2.0 compliant data warehouse.  
In some places, we have supplemented this setup with self-developed macros. We have chosen Snowflake as the target platform, but other databases would also be possible for this setup, in part with a certain amount of adaptation. 
In our view, the use of a data governance tool is highly recommended for the development of a data warehouse that can be maintained in the long term.  
For this reason, we have extended our approach with an adapter to a graphical data governance tool to enable an almost completely automatic construction of the raw vault based on the centrally maintained metadata.

With the data of the fictitious company Willibald and its 13 challenges, a DWH is created with VaultSpeed. An ideal opportunity to see and evaluate the tool in realistic use. The data of the company Willibald will be explained by DDVUG in a previous lecture.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic

Extended Abstract:
For the fictitious company Willibald, a complete data model with data is available that was created by DDVUG and provided with 13 typical challenges. In this presentation, the typical solutions are shown in VaultSpeed and combined to form a complete data warehouse. This will give you an overall impression and the opportunity to get your own comprehensive impression of the tool.
VaultSpeed provides a best-in-class data automation solution, building on the Data Vault 2.0 methodology. Enterprise customers worldwide rely on VaultSpeed to automate multi-source data integration as well as industry-specific metrics stores, on top of the integrated datasets. VaultSpeed is the default SaaS solution for companies looking to simplify the creation and maintenance of their data cloud.
VaultSpeed is headquartered in Leuven, Belgium, with offices in Seattle, USA and Vilnius, Lithuania. For more information, visit www.vaultspeed.com

With the data of the fictitious company Willibald and its 13 challenges, a DWH is created with WhereScape Data Vault Express. An ideal opportunity to see and evaluate the tool in realistic use. The data of the company Willibald will be explained by DDVUG in a previous lecture.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic

Extended Abstract:
For the fictitious company Willibald, a complete data model with data is available that was created by DDVUG and provided with 13 typical challenges. In this presentation, the typical solutions are shown in WhereScape and
combined to form a complete data warehouse. This will give you an overall impression and the opportunity to get your own comprehensive impression of the tool.
WhereScape Data Warehouse Automation software solves one of IT’s biggest problems: how do I build a Data Warehouse with efficiency, reduced risk, and long-term viability?
Using automation, templates, and built-in intelligence, WhereScape increases architect and developer productivity, provides scalability with standards and allows you to take control of your own data whilst fostering collaboration between IT and the business.
With its breadth of technical support, including virtually every source, target, and design methodology, WhereScape helps you design, develop, deploy, and operate your Data Vault (or any other data architecture) at speed with ease.
Create raw vaults, business vaults, and star schema in just a few minutes and realise the performance and quality improvements by reducing hand-offs, tool changes, and other pitfalls of hand-written or partially-automated development efforts.

Being well informed about tools and their capabilities is one thing, but what can I expect when I embark on data warehouse automation?
We have three experts with very different experiences and three completely different approaches who will talk about implementation, reasoning, development speed, costs/benefits, metadata and lessons learned in this panel discussion.

The results of the DWH Automation Challenge are made available in a web-portal. The data and the data model for Willibald are made freely available.
A good reason to celebrate!
A pleasant way to end a long day on DWH automation and an exchange of ideas in a relaxed atmosphere.

Prerequisites: Basic Data Warehouse Knowledge
Level: Basic